Self-Calibrating Monocular Camera for 3D Semantic Landmark Positioning
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Solution Overview
Problem
Current methods for 3D mapping in autonomous vehicles and driver assistance systems require prior knowledge of camera parameters like focal length and principal point, which are often unknown and can change over time, making it costly to expand and update maps efficiently.
Innovation Solution
A method that estimates the camera's focal length and principal point through self-calibration using incremental reconstruction and unsupervised deep learning, allowing for the computation of 3D positions of semantic landmarks like traffic signs using only a camera and GPS, without prior knowledge of these parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prior knowledge of camera parameters (focal length, principal point) is required for 3D mapping, then measurement precision is improved, but device complexity and cost increase due to need for additional sensors like IMU
Solution Approach 1:
The system performs self-calibration by automatically estimating camera parameters (focal length, principal point) from image sequences without requiring external calibration tools or prior knowledge. The camera system serves itself to determine its own parameters through computational methods applied to captured images.
Solution Approach 2:
The patent replaces mechanical/inertial measurement systems (IMU) with computational image processing methods. Instead of using physical sensors to measure camera motion and parameters, the system uses computer vision algorithms to estimate camera parameters and 3D positions purely from image data and GPS information.
2Productivity
If camera parameters are assumed known for crowdsourced mapping, then computation speed is improved, but reliability deteriorates because parameters are actually unknown and may change over time
Solution Approach 1:
The system performs preliminary self-calibration to estimate camera parameters before conducting the main 3D mapping task. By pre-determining focal length and principal point from image sequences, the system ensures accurate parameters are available for subsequent mapping operations without compromising productivity.
Solution Approach 2:
The system dynamically estimates and adapts camera parameters rather than assuming fixed known values. The focal length and principal point are computed as variables that can be determined from image data, allowing the system to handle parameter changes over time while maintaining both efficiency and reliability.
3Measurement precision
If inertial measurement units are added to improve 3D positioning accuracy, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces mechanical/inertial measurement systems (IMU) with computational image processing methods. Instead of using physical sensors to measure camera motion and parameters, the system uses computer vision algorithms to estimate camera parameters and 3D positions purely from image data and GPS information.
Solution Approach 2:
The system performs self-calibration by automatically estimating camera parameters (focal length, principal point) from image sequences without requiring external calibration tools or prior knowledge. The camera system serves itself to determine its own parameters through computational methods applied to captured images.
Data Source
AI summary
Systems arranged to implement methods for positioning a semantic landmark in an image from the real world during a continuous motion of a monocular camera providing said image, using in combination image information from the camera and GPS information, wherein the camera parameters are unknown a priori and are estimated in a self-calibration step, wherein in a subsequent step positioning of the landmarks is completed using one of camera ego motion and depth estimation.